A Dual Active Contour for Improved Snake Performance

Active contours3 (snakes) are a sophisticated approach to contour extraction in image interpretation. They challenge the widely held view that low-level vision tasks such as edge detection are ’bottom-up’ processes; features are extracted from an image and higher level processes interpolate to find a suitable representation. The principal disadvantage with such an approach is its serial nature; errors generated at a low-level are passed on through the system without the possibility of correction. The principal advantage of snakes is that the image data, the initial estimate, desired contour properties and knowledge-based constraints are integrated into a single extraction process. Snakes incorporate a global view of edge detection by assessing continuity and curvature combined with the local edge strength to determine an edge. The motivation for developing a dual active contour is to enhance the snake model by confronting its principal problems: